The present invention relates to methods and apparatus for analyzing test data in determining the effect (e.g., the efficacy and/or toxicity) of one or more drug treatments in addressing a given pathology. In particular, the methods and apparatus are particularly useful in analyzing data of small sample sizes, data that are non-normally distributed, data that are sparse, skewed, heavily-tied, and/or non-continuous. These types of data exist widely in, but not limited to, non-clinical animal studies.
Conventional data analysis for determining the efficacy of drug treatments involve, among other things, the use of one or more statistical methodologies on measured data taken from living organisms, such as mammal studies (e.g., animal studies, human studies, etc.). In the case of animal studies, the lab animals are provided with a particular drug treatment under evaluation, such as ingesting or otherwise receiving an experimental drug, and various parameters and/or characteristics of the animals (measured data) are collected over a particular period of time. There may be a number of treatment groups in the evaluation, including a control group (receiving a placebo) and one or more additional treatments groups receiving the same or different drug treatments. Each treatment group may be referred to as an “ARM”, especially in randomized trials.
The data from the respective ARMS are collected and in some instances the data are manipulated or otherwise processed in order to make them suitable for statistical analysis. The statistical analysis is conventionally applied in order to compare the control ARM to the one or more other ARMS in order to determine whether a given drug treatment ARM has any statistical significance as compared to the control ARM, and whether such difference represents any efficacy as a drug treatment for a particular pathology.
There are many conventional statistical methodologies that have been applied in the analysis of data in drug studies, such as T-tests, analysis of variance (ANOVA) methods, non-parametric ANOVA (such as the Kruskal-Wallis test), the conventional (Asymptotic) Mann-Whitney test, the EXACT Mann-Whitney test, and Multiple Comparison Procedures (MCP) (such as Dunnett's test and Dunn's post test). While these statistical methods have been applied to the drug evaluation process, there are significant problems that may arise when applying a given methodology or set of methodologies to a given drug study. Accordingly, in certain circumstances, the conventional statistical methods and/or application thereof have been found wanting.
Many existing statistical evaluation methods are time consuming and resource demanding. The data sets to be statistically evaluated are often very large and processing times of several hours or even days are common. This is a severe limitation regarding the development of user-interactive statistical analysis application programs. In case it turns out that a particular calculation or sub-calculation, e.g. the execution of one out of a plurality of comparisons of data sets having been derived from different animal groups does not finish for hours or more, the only option left to the user is to stop the whole statistical analysis, thereby loosing results having already obtained from successfully finished sub-calculation steps.